Image processing for distinguishing individuals in groups

ABSTRACT

Cameras capture time-stamped images of predefined areas. At least one image includes a representation of multiple individuals in a group of individuals. Attributes retained with each individual are combined with a limb and pose recognition to properly identify each individual of the group within the image.

BACKGROUND

Current people detection models have difficulty detecting individualpeople from an image depicting a group of people. When an image of avariety of people packed tightly together is sent to existingapproaches, the existing approaches produces output identifying thegroup as a single individual or produces output identifying noindividual in the group.

In fact, detecting multiple individuals in a single image when each ofthe individuals have definable separation between one another is adifficult problem to solve with existing image processing techniques.Detecting individuals when no separation is present in groups is an evenmore difficult problem that heretofore has been unsolved.

SUMMARY

In various embodiments, methods and a system for image processing todetect individuals in groups are presented.

According to an embodiment, a method for image processing to detectindividuals in groups is presented. An image that depicts multipleindividuals in a group is received. Attributes associated with eachindividual are obtained. Limb attributes represented within the imageand identified and the limb attributes are assigned to particular onesof the multiple individuals as limb assignments. The attributes and thelimb assignments are processed against the image, and metadata thatuniquely identifies each of the multiple individuals within the imageare provided based on the processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system for image processing to detectindividuals in groups, according to an example embodiment.

FIG. 2 is a diagram of a method for image processing to detectindividuals in groups, according to an example embodiment.

FIG. 3 is a diagram of another method for image processing to detectindividuals in groups, according to an example embodiment.

FIG. 4 is a diagram of a system for image processing to detectindividuals in groups, according to an example embodiment.

DETAILED DESCRIPTION

FIG. 1 is a diagram of a system 100 for image processing to detectindividuals in groups, according to an example embodiment, according toan example embodiment. It is to be noted that the components are shownschematically in greatly simplified form, with only those componentsrelevant to understanding of the embodiments being illustrated.

Furthermore, the various components (that are identified in the FIG. 1)are illustrated and the arrangement of the components is presented forpurposes of illustration only. It is to be noted that other arrangementswith more or less components are possible without departing from theteachings of image processing to detect individuals in groups, presentedherein and below.

As used herein and below, the terms “customer,” “consumer,” and “user”may be used interchangeably and synonymously.

The system 100 includes a plurality of cameras 110 that capturetime-stamped images of persons and groups of persons (herein after just“defined area images 111”). The system 100 may include a user-operateddevice 130 and one or more transaction terminals 140. The server 120includes executable instructions that execute on one or more hardwareprocessors of the server 120 from a non-transitory computer-readablestorage medium as: an item tracker 121, a person tracker 122, agroup/pose/limb tracker 123, and a transaction manager 124.

It is to be noted that although not illustrated in the FIG. 1, theserver 120 also includes one or more hardware processors, volatile andnon-volatile memory, non-volatile storage, and networking circuitry(such as wired ports and/or wireless transceivers).

It is also to be noted that there may be multiple servers, such that thedifferent elements 121-124 may execute on a same server 120 or multipledifferent servers networked together.

When a customer enters a store or is outside the store in the parkinglot, cameras 110 begin capturing the time-stamped images 111 in frames.In an embodiment, the cameras 110 capture images at a rate of 20 to 30frames per second.

The cameras 110 are preconfigured to capture images 111 of the definedareas based on the field-of-view of the lenses of the cameras 110. Someof the cameras 110 may capture images 111 representing portions of adifferent area that a different one of the cameras 110 captures images111 for. That is, each image 111 can include pixel values that overlapmultiple ones of the defined areas.

Initially, the cameras 110 are situated in locations throughout anenterprise (such as a retail store but can be other enterprises or evena consumer's home). Each camera lens configured to cover one or morepredefined areas of the physical space of the enterprise.

Furthermore, metadata is assigned to each camera 110 to include a uniquecamera identifier, a location identifier (representing the physicallocation that the camera 110 is situated within the enterprise, and oneor more area identifiers (representing the predefined areas that thelens of the camera 110 captures in the images 111).

Each camera 110 provides time stamp and frame stamped images to theserver 120. These images can be streamed over a wired or wirelessconnection between the cameras 110 and the server 120 to a commonlyaccessible storage area on the server 120 that is accessible to the itemtracker 121, the person tracker 122, and the group/pose/limb tracker123. In an embodiment, some of the images when streamed from the cameras110 can be buffered or cached in memory of cache and made accessiblefrom the memory or cache to the item tracker 121, the person tracker122, and the group/pose/limb tracker 123.

Each accessible image 111 includes its metadata (minimally includingwhat was discussed above) with its image 111 on the server 120.

The person tracker 122 processes the pixels of the images to identify aunique person (the actual identity of the person can be unknown but theperson tracker identifies that a person is in the time-stamped images111). Attributes for the unique person are identified as metadata thatpermit the person tracker 122 to quickly and accurately identify theunique person as that person travels through the store and exits thestore from the time-stamped images 111. Attributes can include clothingtype, color, height, width, shoes, extremity features, eye glasses (sunglasses), hats, eye color, etc. A bounding box is placed around theunique person with the generated metadata. As more images 111 arecaptured from the cameras 110, the additional attributes can be added tothe metadata, some existing attributes can be modified as modifiedmetadata, some existing attributes initially believed to be associatedwith the person can be removed as deleted metadata. The person tracker122 may also have its own machine-learning algorithm that is trainedover time, such that the types of attributes represented in the metadatachanges or the pixel information associated with particular metadata ischanged. In this way, the accuracy of the person tracker 122 improveswith time as does the processing throughput associated with producingthe metadata representing the attributes from the images 111.

In an embodiment, the person tracker 122 is configured with facialrecognition to obtain an identity of a person being tracked from theimages.

In a similar manner, the item tracker 121 identifies from the images 111items that are handled by the people being tracked by the person tracker122. That is, the item tracker 121 receives the images, crops off pixelsthat are known to not be associated with the item (such as the pixelsassociated with background objects or a person). Each item includes aunique identifier for tracking even though the actual item may beunknown or unrecognized from the images. That is, (and similar to theperson tracker 122), an item identity (such as the item's description,actual item barcode level of detail, etc.) is unknown in thetime-stamped frames but is still assigned and associated with a uniquetracking identifier in the frames/images 111 so as to distinguishbetween other unknown items of the store or other unknown itemspossessed by the customer. Again, attributes associated with the unknownitem is carried as metadata from frame 111 to frame, so that the itemtracker 121 can quickly identify and crop from later-in-time receivedimages 111 the specific pixels or bounding box being tracked for theunknown item. Attributes can include, color, height, width, edges,bottle shape, item label or packaging characteristics, can shape, boxshape, undefined shape, edges, etc.

Some frames of the images 111 may include multiple persons, such thatsome persons obfuscate or partially conceal other persons in the group.This can occur for a variety of reasons and frequently occurs when apopular area of a store is congested with multiple people. Therefore,single frame can include representations of multiple people in a group.

The person tracker 122 is aware that specific tracked persons aresupposed to be present in the frames but is unable to accuratelyidentify each of the individual persons. In such situations, the persontracker 122 passes the image frame to the group/pose/limb tracker 123along with each individual person's bounding box attributes.

The person tracker 122 maintains coordinates (x and y coordinates) foreach person being tracked in the image frames These coordinates are usedfor tracking a person using a bounding box superimposed on the images111. However, when an image frame 111 is present where the persontracker 122 is expecting to see multiple (more than 1 person) and 1 orno persons are detectable, the person tracker 122 consults thegroup/pose/limb tracker 123.

The group/pose/limb tracker 123 is configured as a deep machine-learningalgorithm that processes stacks of convolutional layers that performlocalization with a combination of classification and regression todetermine the four coordinates of each person in a group from an image.In some cases, this localization includes individual pose or limbrecognition. That is, the group/post/limb tracker 123 is trained toidentify body parts and assign those body parts to the persons that areexpected to be in the image 111 of the group. Assignment can be based onproximity of a limb to a specific person within the image. Thegroup/post/limb tracker 123 returns a labeled version of the image 111of the group having the four coordinates (max x, min x, max y, min y)for each person that the person tracker 122 was expecting to be in theimage 111 of the group. This allows the person tracker 122 to maintainthe bounding boxes for each individual person within the image 111 ofthe group. This is significant because an item being tracked by the itemtracker 121 may be possessed or taken by a particular person in theimage of the group and the association between the correct person withthe item has to be made for a frictionless store implementation of thesystem 100.

The person tracker 122 identifies the number of persons that the persontracker 122 believes to be present in an image 111 of a group andprovides each person's attributes to the group/pose/limb tracker 123 asinput. The group/pose/limb tracker 123 is trained to use thoseattributes as input and produce as output the coordinates of eachexpected person's bounding box for the image 111 of the group. Thegroup/pose/limb tracker 123 may count limbs present in the image of thegroup as a preprocessing step and then uses this limb information alongwith the attributes of each expected person as input to amachine-learning trained algorithm for receiving as output anassociation of each limb with a particular person and a bounding boxwithin the image 111 of the group for each expected person.

This permits the item tracker 121 to properly associate any item takenby a person of the group in the image 111 to be assigned to the properperson being tracked by the person tracker 122. The association ofpossessed items along with the specific person is then processed tonotify the transaction manager 124. The transaction manager 124maintains a shopping cart for each person (known identity or unknownidentity). The transaction manager 124 is notified when items are to beadded or removed from a particular person's shopping cart.

The transaction manager 124 can check out any given person in a varietyof manners. When the person tracker 122 has identified a customer andthe customer is pre-registered with the store and has a registeredpayment method, then the transaction manager can process that paymentmethod when the person is identified by the person tracker 122 asapproaching or leaving an egress point of the store. When the persontracker 122 was unable to obtain the identity of the person, the personcan check out at a transaction terminal 140; the transaction manager 124interacts with an agent process on the transaction terminal 140 andprovides the shopping cart items for payment when the person tracker 122identifies the person as being present at the transaction terminal 140.When the person (identified or unidentified) has a mobile applicationdownloaded to the person's user device 130, the transaction managerinteracts with the mobile application to present the items in theshopping cart and obtain payment from the person. Other approaches forcheckout may be used as well, where the transaction manager 124 and theperson tracker 122 cooperate to obtain payment for known persons(identity known) and for unknown persons (identity unknown).

In an embodiment, the group/pose/limb tracker 123 also is trained todetect the pose (posture, angle of posture, etc.) of each person presentin an image 111 of a group.

The group/pose/limb tracker 123 provides a fine-grain analysis of animage 111 where there person tracker 122 is expecting more than 1 personto be present in that image 111. The group/pose/limb tracker 123 is amachine-learning trained application that returns results permitting theperson tracker 122 and the item tracker 121 to properly identify eachperson's bounding box, pose, and limbs within the image of the group.This allows for distinguishing each individual in a group within animage and substantially improves frictionless store applications and/orsecurity-based applications.

In an embodiment, the transaction terminal 140 is one or more of: APoint-Of-Sale (POS) terminal and a Self-Service Terminal (SST).

In an embodiment, the user-operated device 130 is one or more of: aphone, a tablet, a laptop, and a wearable processing device.

These embodiments and other embodiments are now discussed with referenceto the FIGS. 2-4.

FIG. 2 is a diagram of a method 200 for image processing to detectindividuals in groups, according to an example embodiment. The softwaremodule(s) that implements the method 200 is referred to as an“individual differentiator.” The individual differentiator isimplemented as executable instructions programmed and residing withinmemory and/or a non-transitory computer-readable (processor-readable)storage medium and executed by one or more processors of a device. Theprocessor(s) of the device that executes the individual differentiatorare specifically configured and programmed to process the individualdifferentiator. The individual differentiator has access to one or morenetwork connections during its processing. The network connections canbe wired, wireless, or a combination of wired and wireless.

In an embodiment, the device that executes the individual differentiatoris the server 120. In an embodiment, the server 120 is a cloud-basedserver.

In an embodiment, the individual differentiator is all or somecombination of: the item tracker 121, the person tracker 122, and thegroup/pose/limb tracker 123.

At 210, the individual differentiator receives an image that depictsmultiple individuals within a single frame or image. The image is timeand uniquely frame numbered. Such that each image is unique identifiedfrom the camera that captured the image, the time the image was taken,and the coverage area identifier that is predefined and associated withthe camera (as was discussed above with the FIG. 1).

At 220, the individual differentiator obtains attributes associated witheach individual. These attributes were discussed above with reference tothe item tracker 121 and the person tracker 122. Attributes are pixelvalues determined to represent each individual by the person tracker122.

Accordingly and in an embodiment, at 221, the individual differentiatorobtains the attributes from a person tracker, such as person tracker 122that is tracking each of the individuals in the image and in otherimages captured by the cameras 110.

At 230, the individual differentiator identifies limb attributesrepresenting limbs that are present in the group image (image beingprocessed by the individual differentiator).

In an embodiment of 221 and 230, at 231, the individual differentiatorpasses the image and the individual attributes to a deep learningmachine-learning algorithm that is trained to identify the limbs from animage given the attributes for the individuals. The individualdifferentiator receives as output from the machine-learning algorithmthe limb attributes.

At 240, the individual differentiator assigns the limb attributes toparticular ones of the multiple individuals as limb assignments.

In an embodiment of 231 and 240, at 241, the individual differentiatorreceives the limb assignments with the output provided by themachine-learning algorithm.

In an embodiment, at 242, the individual differentiator identifies posesfor each of individuals depicted within the image. These poses caninclude an individual's angle of orientation, posture, gestures, limbpositions, etc.

In an embodiment of 242, at 243, the individual differentiator assignspose assignments for each of the poses to each of the individualsrepresented within the single image of the group of individuals.

At 250, the individual differentiator processes the attributes and thelimb assignments against the image to distinguish each individualseparately from the other individuals represented within the singlegroup image.

In an embodiment of 243 and 250, at 251, the individual differentiatorprocesses the pose assignments with the limb assignments and theattributes when performing the processing at 250.

In an embodiment of 251, at 252, the individual differentiator passesthe image, the attributes, the limb assignments, and the poseassignments to a deep learning machine-learning algorithm. Themachine-learning algorithm is trained to identify unique individualsbased on the provided input from a single image representing a group ofindividuals.

At 260, the individual differentiator provides metadata that uniquelyidentifies each of the individuals depicted within the image based onthe processing at 250.

In an embodiment of 252 and 260, at 261, the individual differentiatorprovides the metadata as coordinates for unique bounding boxes for theindividuals. Each bounding box including corresponding ones of thecoordinates that define a location/position for each separate individualwithin the image.

In an embodiment, at 262, the individual differentiator provides themetadata as coordinates for uniquely locating each individual within theimage to a person tracker that is tracking each of the individuals inthe images and other images in a frictionless store or security system.In an embodiment, the person tracker is the person tracker 122.

FIG. 3 is a diagram of another method 300 for image processing to detectindividuals in groups, according to an example embodiment. The softwaremodule(s) that implements the method 300 is referred to as a “groupmanager.” The group manager is implemented as executable instructionsprogrammed and residing within memory and/or a non-transitorycomputer-readable (processor-readable) storage medium and executed byone or more processors of a device. The processors that execute thegroup manager are specifically configured and programmed to process thegroup manager. The group manager has access to one or more networkconnections during its processing. The network connections can be wired,wireless, or a combination of wired and wireless.

In an embodiment, the device that executes the group manager is theserver 120. In an embodiment, the server 120 is a cloud processingenvironment.

In an embodiment, the group manager is all of or some combination of:the item tracker 121, the person tracker 122, the group/pose/limbtracker 123, and/or the method 200.

The group manager presents another and in some ways enhanced processingperspective of the method 200 discussed above.

At 310, the group manager receives an expected number of individualsthat are expected or should be present in an image that depicts a groupof individuals within a single image or frame being processed.

In an embodiment, at 311, the group manager receives the expected numberof individuals from a person tracker that is tracking each of theindividuals in the image and other images for a frictionless store orsecurity system. In an embodiment, the person tracker is the persontracker 122.

At 320, the group manager obtains attributes collected for each of theindividuals from processing other images subsequent to the processing at310.

In an embodiment of 311 and 320, at 321, the group manager obtainsattributes from the person tracker.

At 330, the group manager identifies limbs and poses for the individualswithin the image.

In an embodiment, at 331, the group manager uses a machine-learningalgorithm to identify the limbs and the poses. This was discussed above.The machine-learning algorithm is trained to distinguish limbs and posesbased on input provided that includes the attributes, the imagerepresenting a group of individuals, and optionally the expected numberof individuals believed to be present in the image.

At 340, the group manager passes the image, the expected number ofindividuals, and limb and pose attributes for the limbs and poses to atrained deep-learning machine-learning algorithm.

At 350, the group manager acquires as output from the machine-learningalgorithm metadata that defines areas within the image where each of theindividuals are being represented.

In an embodiment, at 351, the group manager obtains the metadata ascoordinate positions for each of the individuals represented within theimage.

In an embodiment of 351, at 352, the group manager identifies thecoordinate positions as sets of coordinate positions. Each set ofcoordinate positions represented as a minimum x-axis position within theimage, a maximum x-axis position within the image, a minimum y-axisposition within the image, and a maximum y-axis position within theimage for each individual detected as being represented within theimage.

In an embodiment of 352, at 353, the group manager uses the sets tosuperimpose bounding boxes around each individual within the image.

At 360, the group manager provides the metadata to a person tracker thatis tracking each of the individuals in the image and other images as aportion of a frictionless store system and/or security system. In anembodiment, the person tracker is the person tracker 122.

In an embodiment, at 361, the group manager provides the metadata to anitem tracker that is tracking items in the image and the other images asanother portion of the frictionless store system and/or the securitysystem. In an embodiment, the item tracker is the item tracker 121.

FIG. 4 is a diagram of a system 400 for image processing to detectindividuals in groups, according to an example embodiment. The system400 includes a variety of hardware components and software components.The software components of the system 400 are programmed and residewithin memory and/or a non-transitory computer-readable medium andexecute on one or more processors of the system 400. The system 400communicates over one or more networks, which can be wired, wireless, ora combination of wired and wireless.

In an embodiment, the system 400 implements, inter alia, the processingdescribed above with the FIGS. 1-3 with respect to the server 120 andthe cameras 110.

In an embodiment, system 400 is the cameras 110 and the server 120.

The system 400 includes a plurality of cameras 401 and a server 402. Theserver 402 includes at least one hardware processor 403 and configuredto execute executable instructions from a non-transitorycomputer-readable storage medium as an individual differentiator 404.

The individual differentiator 404 when executed from the non-transitorycomputer-readable storage medium on the processor 403 is configuredto: 1) receive attributes associated with individuals being tracked inimages; 2) obtain a group image having each of the individuals depictedwithin the group image; 3) identify limbs and poses for each individualwithin the group image; 4) create bounding boxes for each individual,each bounding box representing a particular individual representedwithin the group image based on the attributes, the limbs, and theposes; and 5) provide the bounding boxes to a person tracker that istracking the individuals in the images and the group image.

In an embodiment, the person tracker is the person tracker 122.

In an embodiment, the individual differentiator 404 is furtherconfigured to provide the bounding boxes as sets of coordinates thatidentify each individual within the group image.

In an embodiment, the individual differentiator 404 is furtherconfigured to provide the sets of coordinates to an item tracker that istracking items in the images. In an embodiment, the item tracker is theitem tracker 121.

In an embodiment, the individual differentiator 404 is all or somecombination of: the item tracker 121, the person tracker 122, the groupmanager 123, the transaction manager 124, the method 200, and/or themethod 300.

In an embodiment, the system 100 is deployed as a portion of africtionless store implementation where customers (individuals) shopthrough computer-vision and image processing and items and individualsare associated with one another with a shopping cart maintained for eachindividual. Each individual can checkout and pay for his/her shoppingcart items using any of the above-referenced techniques discussed withthe FIG. 1.

It should be appreciated that where software is described in aparticular form (such as a component or module) this is merely to aidunderstanding and is not intended to limit how software that implementsthose functions may be architected or structured. For example, modulesare illustrated as separate modules, but may be implemented ashomogenous code, as individual components, some, but not all of thesemodules may be combined, or the functions may be implemented in softwarestructured in any other convenient manner.

Furthermore, although the software modules are illustrated as executingon one piece of hardware, the software may be distributed over multipleprocessors or in any other convenient manner.

The above description is illustrative, and not restrictive. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe above description. The scope of embodiments should therefore bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate exemplary embodiment.

The invention claimed is:
 1. A method, comprising: receiving an imagethat depicts multiple individuals in a group; obtaining attributesassociated with each individual; identifying limb attributes representedwithin the image; assigning the limb attributes to particular ones ofthe multiple individuals as limb assignments; processing the attributesand the limb assignments against the image and counting limbs present inthe image based on the limb attributes and attributes of each individualand assigning each limb present in the image to a particular individual;generating a unique bounding box around each unique individual withinthe image, each unique bounding box comprising the corresponding limbsof the corresponding individual; providing metadata associated with eachunique bounding box that uniquely identifies each of the multipleindividuals within the image based on the processing and the generating;maintaining 4 coordinates within the image for each unique bounding boxof each individual in the group based on the metadata; labeling theimage with each detected body part of each individual using thecorresponding 4 coordinates of that individual and the limb assignments;monitoring each individual for being in possession of a particular itemand assigning the particular item to that individual within the imagebased on the labeling and a proximity of the particular item to aparticular limb of that individual; and updating the metadata bymodifying given attributes assigned to a given individual in the groupbased on processing subsequent images of the group.
 2. The method ofclaim 1, wherein obtaining further includes obtaining the attributesfrom a person tracker that is tracking each of the multiple individuals.3. The method of claim 2, wherein identifying further includes passingthe image and the attributes to a machine-learning algorithm andreceiving as output from the machine-learning algorithm the limbattributes.
 4. The method of claim 3, wherein assigning further includesreceiving the limb assignments with the output.
 5. The method of claim1, wherein assigning further includes identifying poses for each of themultiple individuals depicted within the image.
 6. The method of claim5, wherein identifying further includes assigning pose assignments foreach of the poses to each of the multiple individuals.
 7. The method ofclaim 6, wherein processing further includes processing the poseassignments with the limb assignments and the attributes.
 8. The methodof claim 7, wherein processing further includes passing the image, theattributes, the limb assignments, and the pose assignments to amachine-learning algorithm and receiving as output the metadata.
 9. Themethod of claim 8, wherein providing further includes providing themetadata as coordinates for unique bounding boxes for the multipleindividuals, each bounding box including corresponding ones of thecoordinates that defines a location for each individual within theimage.
 10. The method of claim 1, wherein providing further includesproviding the metadata as coordinates for uniquely locating eachindividual within the image to a person tracker that is tracking each ofthe individuals in the images and in other images.
 11. A method,comprising: receiving an expected number of individuals that areexpected to be present in an image depicting of a group of theindividuals; obtaining attributes collected for each of the individuals;identifying limbs and poses for the individuals within the image;counting the limbs present in the image based on limb and poseattributes and attributes of each individual and assigning each limbpresent in the image to a particular individual; passing the image, theexpected number, the attributes, a counted number of limbs from thecounting, and the limb and pose attributes for the limbs and the posesto a machine-learning algorithm; generating a unique bounding box foreach individual within the image; acquiring as output from themachine-learning algorithm metadata defining areas within each uniquebounding box of the image where each of the individuals are represented;providing the metadata and the bounding boxes to a person tracker thatis tracking each of the individuals in the image and other images;maintaining 4 coordinates within the image for each unique bounding boxof each individual in the group based on the metadata; labeling theimage with each detected body part of each individual using thecorresponding 4 coordinates of that individual; monitoring eachindividual for being in possession of a particular item and assigningthe particular item to that individual within the image based on thelabeling and a proximity of the particular item to a particular limb ofthat individual; and updating the metadata by modifying given attributesassigned to a given individual in the group based on processingsubsequent images of the group.
 12. The method of claim 11, whereinreceiving further includes receiving the expected number from the persontracker.
 13. The method of claim 12, wherein obtaining further includesobtaining the attributes from the person tracker.
 14. The method ofclaim 11, wherein identifying further includes using a secondmachine-learning algorithm to identify the limbs and the poses.
 15. Themethod of claim 11, wherein acquiring further includes obtaining themetadata as coordinate positions for each of the individuals representedwithin the image.
 16. The method of claim 15, wherein obtaining furtherincludes identifying the coordinate positions as sets of coordinatepositions each set represent a minimum x-axis position, a maximum x-axisposition, a minimum y-axis position, and a maximum y-axis position foreach individual detected within the image.
 17. The method of claim 16,wherein identifying further includes using the sets to superimposebounding boxes around each individual depicted within the image.
 18. Themethod of claim 11, wherein providing further includes providing themetadata to an item tracker that is tracking items in the image and theother images.
 19. A system, comprising: cameras configured to captureimages within a store; a server that includes a processor; the processorconfigured to execute executable instructions from a non-transitorycomputer-readable storage medium as individual differentiator; thedifferentiator when executed on the processor configured to: receiveattributes associated with individuals being tracked in images; obtain agroup image having each of the individuals depicted within the groupimage; identify limbs and poses for each individual within the groupimage; count the limbs present in the image based on limb and poseattributes and attributes of each individual and assigning each limbpresent in the image to a particular individual; create a uniquebounding box for each individual, each unique bounding box representinga given individual represented within the group image based on theattributes, a counted number of the limbs from the count of the limbs,and the limb and pose attributes; maintain metadata with the images,wherein the metadata comprises identifiers for the attributes, thelimbs, the poses, and the bounding boxes; provide the bounding boxes toa person tracker that is tracking the individuals in the images and thegroup image; maintain 4 coordinates within the image for each uniquebounding box of each individual in the group based on the metadata;label each image with each detected body part of each individual usingthe corresponding 4 coordinates of that individual; monitor eachindividual for being in possession of a particular item and assigningthe particular item to that individual within the corresponding imagebased on labels present within each image and based on a proximity ofthe particular item to a particular limb of that individual; and updatethe metadata by modifying given attributes assigned to a givenindividual in the group based processing the images of the group fromone image to a next image.
 20. The system of claim 19, wherein thedifferentiator is further configured to provide the bounding boxes assets of coordinates that identify each individual within the groupimage.